Splitting anonymization: a novel privacy-preserving approach of social network. Issue 3 (June 2016)

• Record Type: Journal Article
• Authors: Sun, Yongjiao; Yuan, Ye; Wang, Guoren; Cheng, Yurong
• Is Part Of: Knowledge and information systems. Volume 47:Issue 3(2016:Jun.)
• Page Start: 595
• Page End: 623
• Publication Date: 2016-06
• Subjects: Social network -- Privacy preservation -- Attack -- Anonymization -- Predictable error; Expert systems (Computer science) -- Periodicals; Information storage and retrieval systems -- Periodicals; 006.33
• DOI: 10.1007/s10115-015-0855-2
• Languages: English
• ISSNs: 0219-1377

Abstract

Abstract Large amount of personal social information is collected and published due to the rapid development of social network technologies and applications, and thus, it is quite essential to take privacy preservation and prevent sensitive information leakage. Most of current anonymizing techniques focus on the preservation to privacies, but cannot provide accurate answers to utility queries even at a high price. To solve the problem, a novel anonymizing approach, calledsplitting anonymization, is introduced in this paper to point against the contradiction of privacy and utility. This approach provides a high-level preservation to the privacy of social network data that is unknown to attackers, which avoids the low utility caused by the enforced noises on knowledge that is already known to the attackers. Social network processed by splitting anonymization can refuse any direct attack, and these strategies are also safe enough to indirect attacks which are usually more dangerous than direct attacks. Finally, strict theoretical analysis and large amount of evaluation results based on real data sets verified the design of this paper.